|
import gradio as gr |
|
import hopsworks |
|
import joblib |
|
import pandas as pd |
|
import requests |
|
from PIL import Image |
|
|
|
project = hopsworks.login() |
|
fs = project.get_feature_store() |
|
|
|
print("trying to dl model") |
|
mr = project.get_model_registry() |
|
model = mr.get_model("wine_model", version=1) |
|
model_dir = model.download() |
|
model = joblib.load(model_dir + "/wine_model.pkl") |
|
print("Model downloaded") |
|
|
|
|
|
def wine(volatile_acidity, chlorides, density, alcohol): |
|
print("Calling wine function") |
|
df = pd.DataFrame( |
|
[[alcohol, chlorides, volatile_acidity, density]], |
|
columns=["alcohol", "chlorides", "volatile_acidity", "density"], |
|
) |
|
print("Predicting") |
|
print(df) |
|
res = model.predict(df) |
|
print(res) |
|
return res |
|
|
|
|
|
demo = gr.Interface( |
|
fn=wine, |
|
title="Wine Quality Predictive Analytics", |
|
description="Experiment with different values for these properties", |
|
allow_flagging="never", |
|
inputs=[ |
|
gr.Number(label="Alcohol"), |
|
gr.Number(label="Chlorides"), |
|
gr.Number(label="Volatile Acidity"), |
|
gr.Number(label="Density"), |
|
], |
|
outputs=gr.Number(label="Quality"), |
|
) |
|
|
|
demo.launch(debug=True) |
|
|